Wanting to catch up with the methods? Check this paper out!
Understanding where species currently are, and where they are likely to be, is a central question in ecology. One way to obtain such knowledge is to build correlative models that describe how species respond to environmental conditions. Apart from helping answer scientific questions, models of species distributions and range dynamics are frequently used to support different types of environmental decisions.
Now, taking a bunch of species observation records, a bunch of predictors and fitting a model can be done very quickly, supported by a wide range of available user-friendly software packages and tools. A challenge remains however: ensuring that models are carefully built, so that they yield useful predictions. There are many important issues that require attention during model construction (e.g. selection of predictors, resolution, extent…). In the species distribution modelling literature, there is a good selection of papers covering many of these critical topics. One aspect that also needs attention is imperfect detection of species. As species detectability may vary in space and/or time, disregarding the likelihood of species detection can lead to biased inference about species distributions and their dynamics, which can potentially misguide environmental decisions [see this and this].
Over the past 10-15 years, there have been significant methodological advances addressing this problem. A modelling framework has been developed, with models that explicitly describe the observation process, in addition to the latent ecological process (the species distribution, and its drivers). Imperfect detection is a central theme in my research, so last year, I decided to write this review paper (now published in Ecography), with the aim of providing a comprehensive overview of advances in this area. The paper summarizes modelling developments, discusses evidence about effects of imperfect detection and the difficulties of working with it, and concludes with the current outlook for future research and application of these methods.
I wanted this paper to be a helpful tool, not only as a reference for those familiar with the topic, but also as a quick point of entry for those wanting to catch up with these methods. For that purpose, I included this table (click to enlarge) that summarises at a glance some of the key papers in the discipline. I initially built this table as a quick reference guide for myself. I found it useful so I realised it was worth sharing. I hope some of you find it a handy resource too!